Abstract: Systems and methods for transferring learning in sensor devices. Historical time-series measurement samples of one or more parameters associated with a biological function being monitored by the sensor device are received and assigned to clusters. Feature data extracted from the historical time-series measurement samples are used to generate cluster-specific source-domain classifiers for each cluster. Unlabeled time-series measurement samples of the one or more parameters associated with the biological function are received. A cluster-identifier is assigned to each unlabeled target-domain sample, the cluster-identifier including information identifying a cluster-specific source-domain classifier associated with the unlabeled target-domain sample.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: Systems and methods for estimating a replacement status of an air filter in an HVAC system, based on obtaining data correlated with the temperature of air outputted by the HVAC system as a function of time.

Abstract: Systems and methods for monitoring the condition of an air filter installed in an HVAC system and for monitoring the condition of the HVAC system. The monitoring system includes a processing unit configured to receive data representative of at least a first temporal parameter of the HVAC system. The processing unit can process the data to obtain an indication of the condition of the air filter and can also process the data to obtain an indication of the condition of the HVAC system.

Abstract: In some examples, a system for detecting inhibition of a biological assay includes a detection device configured to amplify and detect a target nucleic acid. The detection device is configured to receive a sample comprising a matrix and a quantity of the target nucleic acid and to amplify the target nucleic acid within the sample over a nucleic acid amplification cycle. The detection device is configured to capture a data set including measurements of the nucleic acid collected during the amplification cycle. The system further includes a computing device configured to receive the data set and to apply a machine-learning system to the data set to detect inhibited biological assays that tested negative for the target nucleic acid due to matrix inhibition.

Abstract: In some examples, a system for amplifying and quantifying a target organism present in a sample includes a detection device configured to amplify and detect a nucleic acid associated with the target organism. The detection device configured to receive a sample and to amplify nucleic acid in the sample over an amplification cycle. The detection device is configured to capture a data set including measurements of the nucleic acid collected during the amplification cycle. The system further includes a computing device configured to receive the data set and to apply a machine learning system to the data set. The machine learning system is trained to estimate a quantity of the target organism present in the sample based on the measurements in the data set.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: Systems and methods are provided for optimally determining sensor or infrastructure placement in a fluid network, for determining an anomaly of interest in the fluid network, and for determining sensor coverage in a fluid network, which are based on a model of the fluid network represented by a directed graph.

Abstract: A system for tracking a location of a body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) component, one or more sensors, one or more processors, and a memory device. The memory device includes instructions that, when executed by the one or more processors, cause the one or more processors to determine whether a current location of the BWTD can be determined using the GNSS component. The instructions also cause the one or more processors to determine, based in part on the activity of the wearer of the BWTD and based on a determination that the current location of the BWTD cannot be determined using the GNSS component, whether to perform an action. The instructions also cause the one or more processors to perform the action in response to a determination to perform the action.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, a cellular communication unit, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine whether the BWTD is located on board an aircraft. Execution of the instructions further causes the at least one processor to, responsive to determining that the BWTD is located on board the aircraft: disable the GNSS device and the cellular communication unit; and temporarily refrain from generating an alert that indicates a current location of the BWTD cannot be determined.

Abstract: The present subject matter enables early or real-time detection of anomalies in electric networks. In various applications, the system detects anomalies, such as electricity theft, electricity surge, etc. It solves the difficult-to-detect problems in an electrical network, where anomalies like electricity theft or electrical surge may not be found until it has raised numerous concerns or complaints, or has created a significant impact on infrastructure functionality, service quality, or cost. In addition, the present subject matter decreases the requirement for large number of sensors and provides more cost effective and scalable solutions. The present subject matter provides a method for determining where a detected anomaly is occurring within an electrical network. Variations of the present subject matter include anomaly identification systems for addressing anomalies in large networks.

Abstract: Systems and methods for estimating a replacement status of an air filter in an HVAC system, based on obtaining data correlated with the temperature of air outputted by the HVAC system as a function of time.

Abstract: Systems and methods for detecting the quality of signals captured by a sensor device monitoring a biological function. Sensor data associated with the sensor device is received, the sensor data representing time-series measurement samples of one or more parameters associated with the biological function, the sensor data including usable and unusable samples of the time-series measurements. Data representing two or more features of samples of the time-series measurements is extracted and filtered to reduce outliers in the extracted data based on an expected outlier ratio. A machine learning algorithm is then trained to identify events based on the filtered extracted data.

Abstract: Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Abstract: In an example, a system comprises at least one body-worn tracking device (BWTD) configured to transmit location data that indicates a location of the BWTD and a computing system configured to communicate with the at least one BWTD. The computing system is configured to receive the location data from the at least one BWTD, determine, based on a set of weighted filters associated with at least one monitoring attribute, at least one gradient value corresponding to the location data, where the at least one gradient value represents a level of the at least one monitoring attribute, and perform at least one operation based on the at least one gradient value.

Abstract: In one example, a system includes one or more personal protective equipment (PPE) devices each configured to be worn by a worker, the PPE devices each including one or more sensors that generate activity data indicative of activities of workers operating within one or more work environments. The system also includes a computing device, the computing device configured to: identify, based at least on the activity data, a plurality of clusters of one or more entities, wherein each entity of the entities is associated with one or more of the workers; and output an indication of a difference between performance by a target entity with respect to safety events and performance by the cluster that includes the target entity with respect to safety events.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, a cellular communication unit, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine whether the BWTD is located on board an aircraft. Execution of the instructions further causes the at least one processor to, responsive to determining that the BWTD is located on board the aircraft: disable the GNSS device and the cellular communication unit; and temporarily refrain from generating an alert that indicates a current location of the BWTD cannot be determined.

Abstract: A system for tracking a location of a body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) component, one or more sensors, one or more processors, and a memory device. The memory device includes instructions that, when executed by the one or more processors, cause the one or more processors to determine whether a current location of the BWTD can be determined using the GNSS component. The instructions also cause the one or more processors to determine, based in part on the activity of the wearer of the BWTD and based on a determination that the current location of the BWTD cannot be determined using the GNSS component, whether to perform an action. The instructions also cause the one or more processors to perform the action in response to a determination to perform the action.

Abstract: A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.